Using Bayesian hierarchical modeling for performance evaluation of clinical trial accrual for a cancer center.

Introduction: Slow patient accrual in cancer clinical trials is always a concern. In 2021, the University of Kansas Comprehensive Cancer Center (KUCC), an NCI-designated comprehensive cancer center, implemented the Curated Cancer Clinical Outcomes Database (C3OD) to perform trial feasibility analyses using real-time electronic medical record data. In this study, we proposed a Bayesian hierarchical model to evaluate annual cancer clinical trial accrual performance.

First-Line Immunotherapy with Check-Point Inhibitors: Prospective Assessment of Cognitive Function.

Approximately 40% of patients with cancer are eligible for check-point inhibitor (CPI) therapy. Little research has examined the potential cognitive impact of CPIs. First-line CPI therapy offers a unique research opportunity without chemotherapy-related confounders.

Risk of SARS-CoV-2 Breakthrough Infection in Vaccinated Cancer Patients: A Retrospective Cohort Study.

Although messenger RNA (mRNA) vaccines have established efficacy for prevention of severe SARS-CoV2 infection in the general population, their effectiveness in patients with malignancy, especially those on anti-neoplastic therapies, remains an area of open research. In order to better understand the risk of developing breakthrough SARS-CoV-2 infection and the outcomes associated with breakthrough infection for cancer patients, individual patient data from a curated outcomes database at the University of Kansas were retrospectively reviewed to determine the rate of breakthrough infection during an 8-month period encompassing the height of the delta variant surge. Although the rate of breakthrough infection in cancer patients after two doses of an mRNA vaccine remained low at 1.

The need to study rural cancer outcome disparities at the local level: a retrospective cohort study in Kansas and Missouri.

Background: Rural residence is commonly thought to be a risk factor for poor cancer outcomes. However, a number of studies have reported seemingly conflicting information regarding cancer outcome disparities with respect to rural residence, with some suggesting that the disparity is not present and others providing inconsistent evidence that either urban or rural residence is associated with poorer outcomes. We suggest a simple explanation for these seeming contradictions: namely that rural cancer outcome disparities are related to factors that occur differentially at a local level, such as environmental exposures, lack of access to care or screening, and socioeconomic factors, which differ by type of cancer.

Estimating power for clinical trials with Patient Reported Outcomes - using Item Response Theory.

Objectives: Patient reported outcomes (PRO) are widely used in quality of life (QOL) studies, health outcomes research, and clinical trials. The importance of PRO has been advocated by health authorities. Patient Reported Outcomes Measurement Information System (PROMIS) is a collection of standardized measures of PROs using Item Response Theory (IRT).

Optimizing Retrieval of Biospecimens Using the Curated Cancer Clinical Outcomes Database (C3OD).

To fully support their role in translational and personalized medicine, biorepositories and biobanks must continue to advance the annotation of their biospecimens with robust clinical and laboratory data. Translational research and personalized medicine require well-documented and up-to-date information, but the infrastructure used to support biorepositories and biobanks can easily be out of sync with the host institution. To assist researchers and provide them with accurate pathological, epidemiological, and bio-molecular data, the Biospecimen Repository Core Facility (BRCF) at the University of Kansas Medical Center (KUMC) merges data from medical records, the tumor registry, and pathology reports using the Curated Cancer Clinical Outcomes Database (C3OD).

Relevant Word Order Vectorization for Improved Natural Language Processing in Electronic Health Records.

Electronic health records (EHR) represent a rich resource for conducting observational studies, supporting clinical trials, and more. However, much of the data contains unstructured text, presenting an obstacle to automated extraction. Natural language processing (NLP) can structure and learn from text, but NLP algorithms were not designed for the unique characteristics of EHR.